Hi everyone, I am currently working as an AI Intern and my project is related to AI-based video generation for surgical education and training. The requirement is to generate educational surgery-related videos that are at least 10 minutes long.

I have already researched different approaches and tools, including text-to-video generation, AI avatars, voice synthesis, animation pipelines, and automated video editing, but I am still unable to find a proper workflow that can consistently create high-quality long-form videos suitable for teaching surgical concepts and procedures.

The videos need to include detailed explanations, visuals/animations of surgeries, narration, and educational structure so that they are useful for medical students and trainees. I am looking for guidance from anyone who has experience with:

• AI video generation pipelines
• Long-form educational video creation
• Medical or surgery-related AI content
• Tools/models for animation, narration, and scene generation
• Best workflow for generating 10+ minute videos automatically

If anyone has worked on a similar project or knows useful tools, frameworks, APIs, or research papers, please help me with suggestions or resources. Any guidance would be really appreciated.

Need a free tool for Question paper analysis, which is also interactive. Currently using notebooklm for this Any recommendations?

1. Introduction
Quaynor is a lightweight inference layer that lets you embed open-source LLMs directly into your app. It supports GGUF checkpoints, runs on-device or GPU, and removes dependence on cloud APIs. Bindings are available for Python, Flutter, and React Native, so the same core logic can be reused across platforms.

2. Why it stands out (Key Features)
Fully offline inference: No API keys, no external calls. Models run locally or load from Hugging Face/URLs.
Cross-platform consistency: One chat-style API across Python, Flutter, and React Native.
Streaming + completions: Supports token streaming and full responses.
Tool calling: Grammar-based function execution integrated into native code (Python/mobile).
Optimized hardware usage: Vulkan (Android/Desktop) and Metal (Apple).
Advanced NLP stack: Embeddings and cross-encoder reranking where supported.
Multimodal support: Works with vision/audio models depending on capability.
Production-ready controls: Context window management and efficient inference handling.

3. Who’s using it (Traction)
Early-stage developer adoption via GitHub
Target users:
Indie developers building offline-first apps
Startups focused on privacy-first AI
Mobile engineers embedding on-device AI
Positioned for engineers who want control over inference instead of relying on APIs

4. Pricing Plans
Currently free and open-source
Free Plan
Full access to core inference engine
All bindings (Python, Flutter, React Native)
Local model execution
Streaming, tool calling, embeddings support
Future potential (if monetized)
Hosted model downloads / optimized builds
Enterprise support
Managed updates and performance tuning

5. Offers
No paid plans it’s open source → effectively 100% free access
Early adopters benefit from full capabilities without restrictions

6. Website
GitHub: https://github.com/iBz-04/quaynor
Docs: www.quaynor.site

Explore the repo, run a local model in minutes, and integrate offline AI into your app without relying on any external API.